Predicting Muscle Excitations of the Hand from Kinematic Data

Predicting the muscle excitations of the hand from kinematic data, exclusively, would enable the utilisation of motion capture data for the development of muscle controlled upper-limb prostheses. A method employing an existing musculoskeletal model and a selection of optimisation techniques is proposed for the prediction of muscle excitations of the hand from kinematic data. From 13 participants 62 hours and ten minutes of hand motions in activities of daily living (ADL) have been recorded, from which the functional hand shapes occurring within this time have been determined. A hybrid method utilising a gradient descent (GD) to determine the optimal initial conditions of a, then applied, particle swarm optimisation (PSO) technique is proposed as a means of predicting the muscle excitations of the hand from kinematic data. The proposed method has been applied to the found hand shapes to determine the muscle excitation of ADL. The resultant joint angles were within 16.1 degrees of that from the inputted hand shapes, with a mean correlation between outputted and desired of 0.77. The method has been shown to be applicable with real world recorded data and future modifications to the techniques utilised aims to further improve the accuracy of the output.